Managing Expectations – Part III

August 16, 2014

by Frank Holmes

of U.S. Global Investors

In the first of this three-part series on managing expectations, I discussed the role cycles play in the investment management process. At U.S. Global Investors, we actively monitor both short- and long-term cycles, from the annual seasonality of gold to four-year presidential elections, in order to manage expectations based on historical patterns.

To understand how oscillators work, though, you’ll first need to be familiar with standard deviation and mean reversion.Among other important cycles and patterns that we use are oscillators, which are diagnostic tools that help us measure a security’s upward and downward price volatility. Think of an oscillator as a thermometer; with it, we can accurately take a security’s “temperature.” The knowledge extrapolated from this reading is materially useful in managing expectations, appreciating the dimensionality of a security’s short-term volatility and identifying when to accumulate or trade a stock.

Standard Deviation

Standard deviation, also known by its Greek letter sigma, is a probability tool that gauges a security’s volatility. Specifically, it measures the typical fluctuation of a security around its mean or average return over a period of time ranging from one day to 12 months or more.

In the following bell-shaped curve, the center line represents a security’s average return over a given period of time—one day, 20 days, 60 days or 12 months. To the left and right of the line, the darkest blue sections indicate one standard deviation, or sigma, either above or below the mean; the next lightest, two sigma above or below; and so on.

Standard Deviation Sigma Measures Degree of Variance from Average expectations

click to enlarge

No matter the security, returns can be expected to trade within one sigma of their mean 68 percent of the time. Ninety-five percent of the time they will fluctuate within two sigma, and nearly all of the time they will trade within three.

So why should investors care about this? Generally speaking, the higher the sigma, the higher a security’s volatility; the probability that it will fall back toward the mean also rises. A speculative tech stock, for example, has a greater tendency to have a higher sigma than a blue chip stock. This tells you the tech stock’s returns will fluctuate more widely, more erratically, than the blue chip stock’s.

But sigma is not as black and white as this comparison might suggest. Rather, it more closely resembles multiple shades of color that help investors manage their emotional reactions to the market’s swings and focus instead on the power of using statistics. It’s easy to get pulled into market fears or “irrational exuberance”-to use former Federal Reserve Chairman Alan Greenspan’s phrase-and this probability model helps us be more objective.

To illustrate how these statistics operate in the real world, let’s look at the S&P 500 Index. Over the last ten years, it has had a rolling 12-month standard deviation of 17 percent. This means that if you were to chart its returns over the course of 12 months, you could expect them to stay within ±17 percent from the mean about 70 percent of the time. That’s one sigma. You could also reasonably expect returns to rise or fall within ±34 percent, or two sigma, 95 percent of the time.

Knowing this, it probably wouldn’t be a huge cause for celebration if the S&P 500 rose, say, 8 percent during a 12-month period, since this figure falls within the “normal” one-sigma range. Conversely, a loss of 8 percent wouldn’t be a total disaster. A one-sigma move is a non-event from a historical perspective.

To put this in perspective, the S&P 500 rose about 30 percent last year. This is close to a significant two-sigma move from its 12-month average. Incidentally, 2013 was the index’s best annual performance since 1997.

The most important thing to keep in mind is that, just as we all have different fingerprints, every commodity, every stock, every fund and every index has its own DNA of volatility. The S&P 500 might have a sigma of 17 percent, but over the same 12-month period, the MSCI Emerging Markets Index has a much more volatile 29 percent. Investors must strive to remain objective in the face of emotional factors that move markets and adjust their expectations of how these two indexes behave compared to one another.

Using Weather Statistics to Explain Standard Deviation

As an analogy, consider the extreme temperature fluctuations in Minneapolis-St. Paul, Minnesota. Minneapolis has an average annual temperature of 45 degrees, which sounds pleasant enough. You might think that in such a climate, all you need to get by is a warm jacket. But the picture changes dramatically when you learn that the Twin Cities’ 12-month standard deviation is ±22 degrees. Statistically, this means that for a little over two thirds of the year-68 percent of the time-you can expect the temperature to swing between 23 and 67 degrees. Suddenly that jacket is looking pretty risky. At two standard deviations, there’s a strong probability that the temperature will fall anywhere between a bone-chilling 1 degree-which might very wel l occur, since the average low in January is 2.8 degrees-and 89 degrees. That’s a huge, yawning gap that Minneapolitans must contend with throughout the year.

Compare this to San Antonio, Texas, home of U.S. Global Investors. Here the average temperature is a balmy 70 degrees, with a less-volatile standard deviation of 13 degrees. Even at two sigma-which, again, occurs 95 percent of the time-the temperature in the Alamo City statistically falls anywhere between 42 and 94 degrees, close to the average high in July.

If we’re looking just at temperature fluctuations, Minneapolis resembles the Emerging Markets Index whereas San Antonio behaves more like the S&P 500. Your expectations of “normal,” therefore, will need to be different depending on which of these two cities you reside in or indexes you follow.

Mean Reversion

This leads us to mean reversion, which I discussed in full back in June. Mean reversion is the theory that, although prices might trend up for many years (as in a bull market), or fall for many years (as in a bear market), they tend to move back toward their historic averages eventually. Such elasticity is the basis for knowing when a security is under- or overvalued and when to buy low and sell high. We have just experienced a bull market with the S&P 500 and a bear market with gold stocks. Within these trends, though, are great internal volatility and oscillator tools that monitor these actions. Even in a bull or bear market, we can measure the 20- and 60-day volatility of any kind of security.

Again let’s use Minneapolis as an illustration. We’ve already established its wide-ranging temperature fluctuations throughout the year, from highs reaching the 80s to lows flirting with zero. This being so, it would be unreasonable to expect the weather to remain freezing indefinitely, as is

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